def test_KDE_nearest_window(model, project_task, analysis_model, grid_task):
analysis_model.time_range = (datetime.datetime(2017, 3, 4, 5, 6),
datetime.datetime(2017, 3, 4, 5,
9), None, None)
provider = kde.KDE(model)
provider.space_kernel = 1
provider.space_kernel_model.k = 35
provider.time_kernel = 1
provider.time_kernel_model.days = 12
assert provider.name == "KDE predictor (nearest,window)"
assert provider.settings_string == "35, 12.0 days"
serialise(kde.KDE(model))
subtask = standard_calls(provider, project_task, analysis_model, grid_task)
prediction = subtask(datetime.datetime(2017, 6, 7))
assert_grid_correct(open_cp.kde.KDE.call_args_list)
pred = open_cp.kde.KDE.return_value
assert pred.time_unit == np.timedelta64(1, "s")
assert isinstance(pred.time_kernel, open_cp.kde.ConstantTimeKernel)
assert isinstance(pred.space_kernel,
open_cp.kde.GaussianNearestNeighbourProvider)
assert pred.space_kernel.k == 35
assert_data_correct(pred)
st, en = assert_call_has_start_end_only(pred.predict.call_args_list)
assert st == np.datetime64("2017-06-07T00:00") - np.timedelta64(12, "D")
assert en == np.datetime64("2017-06-07T00:00")
assert prediction is pred.predict.return_value
def test_KDE_quad_decay(model, project_task, analysis_model, grid_task):
analysis_model.time_range = (datetime.datetime(2017, 3, 4, 5, 6),
datetime.datetime(2017, 3, 4, 5,
9), None, None)
provider = kde.KDE(model)
provider.time_kernel = 3
provider.time_kernel_model.scale = 12.3
assert provider.name == "KDE predictor (scipy,quadratic)"
assert provider.settings_string == "12.3 days"
serialise(kde.KDE(model))
subtask = standard_calls(provider, project_task, analysis_model, grid_task)
prediction = subtask(datetime.datetime(2017, 6, 7))
assert_grid_correct(open_cp.kde.KDE.call_args_list)
pred = open_cp.kde.KDE.return_value
assert pred.time_unit == np.timedelta64(1, "s")
assert isinstance(pred.time_kernel, open_cp.kde.QuadDecayTimeKernel)
assert pred.time_kernel.scale == pytest.approx(12.3)
assert isinstance(pred.space_kernel, open_cp.kde.GaussianBaseProvider)
assert_data_correct(pred)
st, en = assert_call_has_start_end_only(pred.predict.call_args_list)
assert st == np.datetime64("2017-06-07T00:00") - np.timedelta64(
24 * 60 * 60, "s") * 12.3 * 32
assert en == np.datetime64("2017-06-07T00:00")
assert prediction is pred.predict.return_value
def test_KDE_subtask(model, project_task, analysis_model, grid_task):
analysis_model.time_range = (datetime.datetime(2017, 3, 4, 5, 6),
datetime.datetime(2017, 3, 4, 5,
9), None, None)
provider = kde.KDE(model)
subtask = standard_calls(provider, project_task, analysis_model, grid_task)
prediction = subtask(datetime.datetime(2017, 6, 7))
assert_grid_correct(open_cp.kde.KDE.call_args_list)
pred = open_cp.kde.KDE.return_value
assert pred.time_unit == np.timedelta64(1, "s")
assert isinstance(pred.time_kernel, open_cp.kde.ConstantTimeKernel)
assert isinstance(pred.space_kernel, open_cp.kde.GaussianBaseProvider)
assert_data_correct(pred)
st, en = assert_call_has_start_end_only(pred.predict.call_args_list)
assert st == np.datetime64("2017-03-04T05:06")
assert en == np.datetime64("2017-03-04T05:09")
assert prediction is pred.predict.return_value
def test_Kde(model, project_task, analysis_model, grid_task):
provider = kde.KDE(model)
assert provider.name == "KDE predictor (scipy,training)"
assert provider.settings_string == ""
standard_calls(provider, project_task, analysis_model, grid_task)
def test_Kde_serialise(model, project_task, analysis_model, grid_task):
serialise(kde.KDE(model))